def shot_bmag_stdnorm(refPos, window, filt, max_probe_number=8):
""" Outputs the tools.correlation norm array w.r.t shot. The norm is product of
the standard deviations of the two windowed and filtered bmag signals. """
maxRange = tools.gen_max_probe_range(refPos,
max_probe_number=max_probe_number)
type(maxRange)
normWFArray = np.zeros([maxRange, 25])
posArray = [*range(refPos, 16, 2)]
for posIndex, pos in enumerate(posArray):
for shot in range(0, 25):
Br_1, Bt_1, Bz_1, time = data03.load_data(refPos, shot)
Br_2, Bt_2, Bz_2, time = data03.load_data(pos, shot)
start_Dex = BMX.finding_Index_Time(time * 1e-6, window[0])
end_Dex = BMX.finding_Index_Time(time * 1e-6, window[1])
Bmag1 = np.sqrt(Br_1**2 + Bt_1**2 + Bz_1**2)
Bmag2 = np.sqrt(Br_2**2 + Bt_2**2 + Bz_2**2)
fBmag1 = tools.HPF(Bmag1, filt)
fBmag2 = tools.HPF(Bmag2, filt)
wfBmag1 = fBmag1[start_Dex:end_Dex]
wfBmag2 = fBmag2[start_Dex:end_Dex]
normWFBmag = np.sqrt(np.var(wfBmag1) * np.var(wfBmag2))
normWFArray[posIndex, shot] = normWFBmag
return normWFArray
def single_shot_bmag_temporal_taylor_y_intercepts(refPos, shot, filt, window, norm='ref', window2=[0, 13]):
wfMagCorr, tau = btc.gen_shot_pos_bmag_correlation(
refPos=refPos, pos=refPos, shot=shot, filt=filt, window=window, norm=norm)
separations = tof.get_tau_spatial(tau, shot)
startDex = bmx.finding_Index_Time(separations*1e-6, window2[0])
endDex = bmx.finding_Index_Time(separations*1e-6, window2[1])
yTSArray, maxSeparationArray, uncertainty, _ = y_intercepts(
wfMagCorr[startDex:endDex], separations[startDex:endDex])
return yTSArray, maxSeparationArray, uncertainty
def get_windowed_bmag(pos, shot, window):
""" Output the windowed bmag and time arrays of {shot} at {pos}."""
bmag, time = get_bmag(pos, shot)
start_dex = BMX.finding_Index_Time(time * 1e-6, window[0])
end_dex = BMX.finding_Index_Time(time * 1e-6, window[1])
wbmag = bmag[start_dex:end_dex]
time = time[start_dex:end_dex]
return wbmag, time
def single_shot_abs_bmag_temporal_taylor_y_intercepts(refPos, shot, filt, window, norm='ref', window2=[0, 13]):
""" Outputs the y_intercept array from the temporal correlations of the absolute Bmag with respect
to shot.
"""
wfMagCorr, tau = btc.gen_shot_pos_bmag_correlation(
refPos=refPos, pos=refPos, shot=shot, filt=filt, window=window, norm=norm)
separations = tof.get_tau_spatial(tau, shot)
startDex = bmx.finding_Index_Time(separations*1e-6, window2[0])
endDex = bmx.finding_Index_Time(separations*1e-6, window2[1])
yTSArray, maxSeparationArray, uncertainty, _ = y_intercepts(
np.abs(wfMagCorr[startDex:endDex]), separations[startDex:endDex])
return yTSArray, maxSeparationArray, uncertainty
def compute_time_delay(refPos, pos, shot, window=[75, 150], norm='ref', normalized=False):
""" Output the time delay corresponding to the maximum correlation.
"""
magwCorr, tau = get_shot_correlation(
refPos, pos, shot, window, norm, normalized)
zeroDex = bmx.finding_Index_Time(tau*1e-6, -10)
limitDex = bmx.finding_Index_Time(tau*1e-6, 10)
magwCorr = magwCorr[zeroDex:limitDex]
tau = tau[zeroDex:limitDex]
maxIndex = np.where(magwCorr == np.max(magwCorr))[0][0]
timeDelay = tau[maxIndex]
return timeDelay
def gen_shot_bmag_spatial_correlation(refPos,
shot,
filt=3e4,
window=[75, 150],
norm='ref'):
normArray = norms.which_norm(norm, refPos, window, filt)
def norm_value(norm, posIndex, shot):
""" Wrapper function to make choosing the norm value easier"""
if norm.lower() == 'ref':
normValue = normArray[shot]
else:
normValue = normArray[posIndex, shot]
return normValue
magShotArray = np.asarray([])
for pos2Index, pos2 in enumerate([*range(refPos, 16, 2)]):
Br_1, Bt_1, Bz_1, time = data03.load_data(refPos, shot)
Br_2, Bt_2, Bz_2, time = data03.load_data(pos2, shot)
if (pos2 == refPos):
start_Dex = BMX.finding_Index_Time(time * 1e-6, window[0])
if (window[1] != 190):
end_Dex = BMX.finding_Index_Time(time * 1e-6, window[1])
else:
end_Dex = -1
Bmag1 = np.sqrt(Br_1**2 + Bt_1**2 + Bz_1**2)
Bmag2 = np.sqrt(Br_2**2 + Bt_2**2 + Bz_2**2)
Bmag1 = tools.HPF(Bmag1, filt)
Bmag2 = tools.HPF(Bmag2, filt)
time = time[start_Dex:end_Dex]
Bmag1 = Bmag1[start_Dex:end_Dex]
Bmag2 = Bmag2[start_Dex:end_Dex]
corrMagHold, _ = tools.correlation(Bmag1, Bmag2, time, mode='valid')
# corr_MagHold = np.abs(corr_MagHold)
normValue = norm_value(norm=norm, posIndex=pos2Index, shot=shot)
normCorrMagHold = corrMagHold / normValue
magShotArray = np.append(magShotArray, normCorrMagHold)
return magShotArray